Roughly 4. households — or 5. Gruenberg said in a statement. A lack of banking options delayed some households from getting federal payments aimed at helping the country weather the economic fallout from the COVID health crisis. Battle against predatory lending: Mississippi social justice firm fights payday 'predatory lending' in low-income communities. Checks arrived late for some of the unbanked: For 'unbanked' Americans, pandemic stimulus checks arrived slowly and with higher fees.

But that could change. The FDIC initiated an educational campaign to get more Americans to open an account to enable the direct deposit of those funds. Few-Shot Fast-Adaptive Anomaly Detection. Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees. Exploration via Planning for Information about the Optimal Trajectory. Theoretical analysis of deep neural networks for temporally dependent observations. Provably sample-efficient RL with side information about latent dynamics.

In What Ways Are Deep Neural Networks Invariant and How Should We Measure This? Truncated proposals for scalable and hassle-free simulation-based inference. Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. Policy Optimization with Linear Temporal Logic Constraints. Fast Instrument Learning with Faster Rates. Outlier-Robust Sparse Estimation via Non-Convex Optimization. Gradient Methods Provably Converge to Non-Robust Networks. Operator Splitting Value Iteration.

Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning. Learning from Stochastically Revealed Preference. Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems. Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions. Distributed Inverse Constrained Reinforcement Learning for Multi-agent Systems. Uplifting Bandits. Poisson Flow Generative Models. Multi-agent Dynamic Algorithm Configuration.

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging. A Theory of PAC Learnability under Transformation Invariances. Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation. Biologically plausible solutions for spiking networks with efficient coding.

Phase transitions in when feedback is useful. Simple and Optimal Greedy Online Contention Resolution Schemes. Private Estimation with Public Data. Robustness to Label Noise Depends on the Shape of the Noise Distribution. Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification.

Minimax Optimal Online Imitation Learning via Replay Estimation. Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss. List-Decodable Sparse Mean Estimation.

Finite-Time Last-Iterate Convergence for Learning in Multi-Player Games. Depth is More Powerful than Width with Prediction Concatenation in Deep Forest. Neural Abstractions.

Random Rank: The One and Only Strategyproof and Proportionally Fair Randomized Facility Location Mechanism. Smooth Fictitious Play in Stochastic Games with Perturbed Payoffs and Unknown Transitions. The Query Complexity of Cake Cutting. Learning Very Simple Generative Models Is Hard. Cryptographic Hardness of Learning Halfspaces with Massart Noise. Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions. Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling.

Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games. On Optimal Learning Under Targeted Data Poisoning. Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs.

Better Best of Both Worlds Bounds for Bandits with Switching Costs. Coreset for Line-Sets Clustering. On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood. Society of Agents: Regrets Bounds of Concurrent Thompson Sampling. Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization.

Kernel Multimodal Continuous Attention. Empirical Gateaux Derivatives for Causal Inference. Bayesian Persuasion for Algorithmic Recourse. Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. The Hessian Screening Rule. Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity.

Will Bilevel Optimizers Benefit from Loops. Expected Improvement for Contextual Bandits. Fast Neural Kernel Embeddings for General Activations. Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks. Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth.

Understanding Deep Contrastive Learning via Coordinate-wise Optimization. Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning.

Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model. Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation. Towards Versatile Embodied Navigation. CASA: Category-agnostic Skeletal Animal Reconstruction. Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media. Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling. Brain Network Transformer.

Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems. SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks. The Phenomenon of Policy Churn. Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis.

Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis. Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats.

To update or not to update? Neurons at equilibrium in deep models. Improving Intrinsic Exploration with Language Abstractions. Masked Generative Adversarial Networks are Data-Efficient Generation Learners. Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention. Theseus: A Library for Differentiable Nonlinear Optimization. Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems.

Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning. S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces. Decoupling Features in Hierarchical Propagation for Video Object Segmentation. Is Out-of-Distribution Detection Learnable? QUARK: Controllable Text Generation with Reinforced Unlearning. Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution.

Homomorphic Matrix Completion. Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models. Characterizing the Ventral Visual Stream with Response-Optimized Neural Encoding Models. GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images.

What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods. CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders. VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives. Accelerated Training of Physics-Informed Neural Networks PINNs using Meshless Discretizations. On the Importance of Gradient Norm in PAC-Bayesian Bounds.

Temporally Disentangled Representation Learning. Natural gradient enables fast sampling in spiking neural networks. Triangulation candidates for Bayesian optimization.

Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings. A permutation-free kernel two-sample test. Exponential Family Model-Based Reinforcement Learning via Score Matching. Learning with little mixing. Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds. Communication Efficient Federated Learning for Generalized Linear Bandits. On Scrambling Phenomena for Randomly Initialized Recurrent Networks.

Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks. Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities. Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search. Measuring Data Reconstruction Defenses in Collaborative Inference Systems. Function Classes for Identifiable Nonlinear Independent Component Analysis. Towards Disentangling Information Paths with Coded ResNeXt.

Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective. On Robust Multiclass Learnability. Generalised Mutual Information for Discriminative Clustering. A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning. Anytime-Valid Inference For Multinomial Count Data.

OpenOOD: Benchmarking Generalized Out-of-Distribution Detection. FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning. NAS-Bench Benchmarking Neural Architecture Search on Diverse Tasks.

ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography. Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation. How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? ActionNet: A Multimodal Dataset for Human Activities Using Wearable Sensors in a Kitchen Environment. Breaking Bad: A Dataset for Geometric Fracture and Reassembly. FlyView: a bio-inspired optical flow truth dataset for visual navigation using panoramic stereo vision.

Chartalist: Labeled Graph Datasets for UTXO and Account-based Blockchains. SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments. WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models.

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction. xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery. CEDe: A collection of expert-curated datasets with atom-level entity annotations for Optical Chemical Structure Recognition. FETA: Towards Specializing Foundational Models for Expert Task Applications.

CAESAR: An Embodied Simulator for Generating Multimodal Referring Expression Datasets. Evaluating Out-of-Distribution Performance on Document Image Classifiers. Learning Long-Term Crop Management Strategies with CyclesGym. pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning. Myriad: a real-world testbed to bridge trajectory optimization and deep learning. Deep Limits and a Cut-Off Phenomenon for Neural Networks. Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems.

Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing. The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks. Foolish Crowds Support Benign Overfitting. Conformal Prediction in Poster Session 2 []. Alignment-guided Temporal Attention for Video Action Recognition. A Nonconvex Framework for Structured Dynamic Covariance Recovery. TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels.

On Feature Learning in the Presence of Spurious Correlations. Spatial Mixture-of-Experts. Exploring Length Generalization in Large Language Models. Neural Topological Ordering for Computation Graphs. Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation. Offline Multi-Agent Reinforcement Learning with Knowledge Distillation. Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments. Maximum Class Separation as Inductive Bias in One Matrix.

Training Uncertainty-Aware Classifiers with Conformalized Deep Learning. Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent. FlowHMM: Flow-based continuous hidden Markov models.

Merging Models with Fisher-Weighted Averaging. Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations.

Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members. Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology. Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels. Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse.

Ordered Subgraph Aggregation Networks. Infinite Recommendation Networks: A Data-Centric Approach. Diversity vs. Recognizability: Human-like generalization in one-shot generative models. Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers. Improving Transformer with an Admixture of Attention Heads. Preservation of the Global Knowledge by Not-True Distillation in Federated Learning. Information-Theoretic GAN Compression with Variational Energy-based Model.

VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning. CLEAR: Generative Counterfactual Explanations on Graphs. Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE. Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation.

A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning. ClimbQ: Class Imbalanced Quantization Enabling Robustness on Efficient Inferences. Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social Text Classification. SIREN: Shaping Representations for Detecting Out-of-Distribution Objects. Efficient and Effective Multi-task Grouping via Meta Learning on Task Combinations.

DENSE: Data-Free One-Shot Federated Learning. SparCL: Sparse Continual Learning on the Edge. Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again. Multi-modal Grouping Network for Weakly-Supervised Audio-Visual Video Parsing. Searching for Better Spatio-temporal Alignment in Few-Shot Action Recognition. Learning Generalizable Part-based Feature Representation for 3D Point Clouds.

Coordinates Are NOT Lonely - Codebook Prior Helps Implicit Neural 3D representations. Set-based Meta-Interpolation for Few-Task Meta-Learning. Zero-Shot Video Question Answering via Frozen Bidirectional Language Models. Masked Autoencoders As Spatiotemporal Learners. Uncovering the Structural Fairness in Graph Contrastive Learning. AutoMS: Automatic Model Selection for Novelty Detection with Error Rate Control. Learning Best Combination for Efficient N:M Sparsity. C-Mixup: Improving Generalization in Regression.

GAPX: Generalized Autoregressive Paraphrase-Identification X. Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models. GAMA: Generative Adversarial Multi-Object Scene Attacks.

Composite Feature Selection Using Deep Ensembles. Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods. Discovered Policy Optimisation.

Learning Structure from the Ground upHierarchical Representation Learning by Chunking. Amortized Inference for Heterogeneous Reconstruction in Cryo-EM. Neural Approximation of Graph Topological Features. Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment.

Improving Variational Autoencoders with Density Gap-based Regularization. End-to-end Stochastic Optimization with Energy-based Model.

Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions. Advancing Model Pruning via Bi-level Optimization. Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. Is Integer Arithmetic Enough for Deep Learning Training?

Dense Interspecies Face Embedding. Learning State-Aware Visual Representations from Audible Interactions. VITA: Video Instance Segmentation via Object Token Association. Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy.

Pyramid Attention For Source Code Summarization. An Empirical Study on Disentanglement of Negative-free Contrastive Learning. Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever. Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer. Get More at Once: Alternating Sparse Training with Gradient Correction.

Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture. Synergy-of-Experts: Collaborate to Improve Adversarial Robustness. Accelerating Certified Robustness Training via Knowledge Transfer. Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency. Self-supervised Amodal Video Object Segmentation. Rethinking Image Restoration for Object Detection. Unsupervised Learning of Shape Programs with Repeatable Implicit Parts.

ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler. Learning dynamics of deep linear networks with multiple pathways. FP8 Quantization: The Power of the Exponent. Pushing the limits of fairness impossibility: Who's the fairest of them all?

Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. Pseudo-Riemannian Graph Convolutional Networks. PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. Learning Mixed Multinomial Logits with Provable Guarantees. Truly Deterministic Policy Optimization. A Consistent and Differentiable Lp Canonical Calibration Error Estimator.

Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator. Reinforcement Learning with Logarithmic Regret and Policy Switches. Finite Sample Analysis Of Dynamic Regression Parameter Learning.

Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. Network change point localisation under local differential privacy. When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits.

Diversified Recommendations for Agents with Adaptive Preferences. Parameter-free Regret in High Probability with Heavy Tails. Shape And Structure Preserving Differential Privacy. Multi-layer State Evolution Under Random Convolutional Design.

Graph Neural Networks with Adaptive Readouts. On the detrimental effect of invariances in the likelihood for variational inference. Causally motivated multi-shortcut identification and removal. Learning Predictions for Algorithms with Predictions. Near-Optimal Collaborative Learning in Bandits. Incentivizing Combinatorial Bandit Exploration. BR-SNIS: Bias Reduced Self-Normalized Importance Sampling.

Differentially Private Learning with Margin Guarantees. Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes. Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction.

Pay attention to your loss : understanding misconceptions about Lipschitz neural networks. What You See is What You Get: Principled Deep Learning via Distributional Generalization. Data-Driven Model-Based Optimization via Invariant Representation Learning. Learning Manifold Dimensions with Conditional Variational Autoencoders. TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets.

Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations. CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification.

Teacher Forcing Recovers Reward Functions for Text Generation. Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure. MGNNI: Multiscale Graph Neural Networks with Implicit Layers. Gaussian Copula Embeddings. On Learning Fairness and Accuracy on Multiple Subgroups. Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning. Sequencer: Deep LSTM for Image Classification. Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts.

Sound and Complete Verification of Polynomial Networks. DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing. Generalized Laplacian Eigenmaps. Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances.

Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning. RKHS-SHAP: Shapley Values for Kernel Methods. Temporally-Consistent Survival Analysis. ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On.

Diffusion-based Molecule Generation with Informative Prior Bridges. Learning with convolution and pooling operations in kernel methods. Optimizing Relevance Maps of Vision Transformers Improves Robustness. Unsupervised Causal Generative Understanding of Images. SelecMix: Debiased Learning by Contradicting-pair Sampling. Controllable Text Generation with Neurally-Decomposed Oracle.

Explain My Surprise: Learning Efficient Long-Term Memory by predicting uncertain outcomes. Rethinking Resolution in the Context of Efficient Video Recognition. Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment. Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning. Infinite-Fidelity Coregionalization for Physical Simulation. Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost.

Private Set Generation with Discriminative Information. Private Synthetic Data for Multitask Learning and Marginal Queries. Deep Equilibrium Approaches to Diffusion Models. A Reparametrization-Invariant Sharpness Measure Based on Information Geometry. A Statistical Online Inference Approach in Averaged Stochastic Approximation.

Understanding Square Loss in Training Overparametrized Neural Network Classifiers. A Deep Reinforcement Learning Framework for Column Generation.

Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations. Models Out of Line: A Fourier Lens on Distribution Shift Robustness. Pre-Trained Language Models for Interactive Decision-Making.

The Neural Testbed: Evaluating Joint Predictions. Is a Modular Architecture Enough? A theory of weight distribution-constrained learning. Fast Bayesian Estimation of Point Process Intensity as Function of Covariates. PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits. Parallel Tempering With a Variational Reference. Embrace the Gap: VAEs Perform Independent Mechanism Analysis.

Learning Deep Input-Output Stable Dynamics. Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation. Hyperbolic Embedding Inference for Structured Multi-Label Prediction. Surprising Instabilities in Training Deep Networks and a Theoretical Analysis.

Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. Boosting the Performance of Generic Deep Neural Network Frameworks with Log-supermodular CRFs. SnAKe: Bayesian Optimization with Pathwise Exploration.

On Measuring Excess Capacity in Neural Networks. Robust Reinforcement Learning using Offline Data. Thinned random measures for sparse graphs with overlapping communities. Value Function Decomposition for Iterative Design of Reinforcement Learning Agents. The Privacy Onion Effect: Memorization is Relative. Evaluating Graph Generative Models with Contrastively Learned Features.

Weakly supervised causal representation learning. Kernel Memory Networks: A Unifying Framework for Memory Modeling. Wavelet Score-Based Generative Modeling. Monte Carlo Tree Descent for Black-Box Optimization.

Californians are much more pessimistic about the direction of the country than they are about the direction of the state.

Majorities across all demographic groups and partisan groups, as well as across regions, are pessimistic about the direction of the United States. A wide partisan divide exists: most Democrats and independents say their financial situation is about the same as a year ago, while solid majorities of Republicans say they are worse off.

Regionally, about half in the San Francisco Bay Area and Los Angeles say they are about the same, while half in the Central Valley say they are worse off; residents elsewhere are divided between being worse off and the same.

The shares saying they are worse off decline as educational attainment increases. Strong majorities across partisan groups feel negatively, but Republicans and independents are much more likely than Democrats to say the economy is in poor shape. Today, majorities across partisan, demographic, and regional groups say they are following news about the gubernatorial election either very or fairly closely. In the upcoming November 8 election, there will be seven state propositions for voters.

Due to time constraints, our survey only asked about three ballot measures: Propositions 26, 27, and For each, we read the proposition number, ballot, and ballot label. Two of the state ballot measures were also included in the September survey Propositions 27 and 30 , while Proposition 26 was not.

This measure would allow in-person sports betting at racetracks and tribal casinos, requiring that racetracks and casinos offering sports betting make certain payments to the state to support state regulatory costs. It also allows roulette and dice games at tribal casinos and adds a new way to enforce certain state gambling laws.

Fewer than half of likely voters say the outcome of each of these state propositions is very important to them. Today, 21 percent of likely voters say the outcome of Prop 26 is very important, 31 percent say the outcome of Prop 27 is very important, and 42 percent say the outcome of Prop 30 is very important. Today, when it comes to the importance of the outcome of Prop 26, one in four or fewer across partisan groups say it is very important to them.

About one in three across partisan groups say the outcome of Prop 27 is very important to them. Fewer than half across partisan groups say the outcome of Prop 30 is very important to them. When asked how they would vote if the election for the US House of Representatives were held today, 56 percent of likely voters say they would vote for or lean toward the Democratic candidate, while 39 percent would vote for or lean toward the Republican candidate.

Democratic candidates are preferred by a point margin in Democratic-held districts, while Republican candidates are preferred by a point margin in Republican-held districts. Abortion is another prominent issue in this election. When asked about the importance of abortion rights, 61 percent of likely voters say the issue is very important in determining their vote for Congress and another 20 percent say it is somewhat important; just 17 percent say it is not too or not at all important.

With the controlling party in Congress hanging in the balance, 51 percent of likely voters say they are extremely or very enthusiastic about voting for Congress this year; another 29 percent are somewhat enthusiastic while 19 percent are either not too or not at all enthusiastic.

Today, Democrats and Republicans have about equal levels of enthusiasm, while independents are much less likely to be extremely or very enthusiastic. As Californians prepare to vote in the upcoming midterm election, fewer than half of adults and likely voters are satisfied with the way democracy is working in the United States—and few are very satisfied. Satisfaction was higher in our February survey when 53 percent of adults and 48 percent of likely voters were satisfied with democracy in America.

Today, half of Democrats and about four in ten independents are satisfied, compared to about one in five Republicans.

Notably, four in ten Republicans are not at all satisfied. In addition to the lack of satisfaction with the way democracy is working, Californians are divided about whether Americans of different political positions can still come together and work out their differences.

Forty-nine percent are optimistic, while 46 percent are pessimistic. Today, in a rare moment of bipartisan agreement, about four in ten Democrats, Republicans, and independents are optimistic that Americans of different political views will be able to come together.

Notably, in , half or more across parties, regions, and demographic groups were optimistic. Today, about eight in ten Democrats—compared to about half of independents and about one in ten Republicans—approve of Governor Newsom. Across demographic groups, about half or more approve of how Governor Newsom is handling his job. Approval of Congress among adults has been below 40 percent for all of after seeing a brief run above 40 percent for all of Democrats are far more likely than Republicans to approve of Congress.

Fewer than half across regions and demographic groups approve of Congress. Approval in March was at 44 percent for adults and 39 percent for likely voters. Across demographic groups, about half or more approve among women, younger adults, African Americans, Asian Americans, and Latinos. Views are similar across education and income groups, with just fewer than half approving.

Approval in March was at 41 percent for adults and 36 percent for likely voters. Across regions, approval reaches a majority only in the San Francisco Bay Area. Across demographic groups, approval reaches a majority only among African Americans. This map highlights the five geographic regions for which we present results; these regions account for approximately 90 percent of the state population.

Residents of other geographic areas in gray are included in the results reported for all adults, registered voters, and likely voters, but sample sizes for these less-populous areas are not large enough to report separately. The PPIC Statewide Survey is directed by Mark Baldassare, president and CEO and survey director at the Public Policy Institute of California.

Coauthors of this report include survey analyst Deja Thomas, who was the project manager for this survey; associate survey director and research fellow Dean Bonner; and survey analyst Rachel Lawler. The Californians and Their Government survey is supported with funding from the Arjay and Frances F. Findings in this report are based on a survey of 1, California adult residents, including 1, interviewed on cell phones and interviewed on landline telephones.

The sample included respondents reached by calling back respondents who had previously completed an interview in PPIC Statewide Surveys in the last six months. Interviews took an average of 19 minutes to complete. Interviewing took place on weekend days and weekday nights from October 14—23, Cell phone interviews were conducted using a computer-generated random sample of cell phone numbers. Additionally, we utilized a registration-based sample RBS of cell phone numbers for adults who are registered to vote in California.

All cell phone numbers with California area codes were eligible for selection. After a cell phone user was reached, the interviewer verified that this person was age 18 or older, a resident of California, and in a safe place to continue the survey e. Cell phone respondents were offered a small reimbursement to help defray the cost of the call.

Cell phone interviews were conducted with adults who have cell phone service only and with those who have both cell phone and landline service in the household. Landline interviews were conducted using a computer-generated random sample of telephone numbers that ensured that both listed and unlisted numbers were called.

Additionally, we utilized a registration-based sample RBS of landline phone numbers for adults who are registered to vote in California. All landline telephone exchanges in California were eligible for selection.

For both cell phones and landlines, telephone numbers were called as many as eight times. When no contact with an individual was made, calls to a number were limited to six. Also, to increase our ability to interview Asian American adults, we made up to three additional calls to phone numbers estimated by Survey Sampling International as likely to be associated with Asian American individuals.

Accent on Languages, Inc. The survey sample was closely comparable to the ACS figures. To estimate landline and cell phone service in California, Abt Associates used state-level estimates released by the National Center for Health Statistics—which used data from the National Health Interview Survey NHIS and the ACS.

The estimates for California were then compared against landline and cell phone service reported in this survey. We also used voter registration data from the California Secretary of State to compare the party registration of registered voters in our sample to party registration statewide.

The sampling error, taking design effects from weighting into consideration, is ±3. This means that 95 times out of , the results will be within 3. The sampling error for unweighted subgroups is larger: for the 1, registered voters, the sampling error is ±4. For the sampling errors of additional subgroups, please see the table at the end of this section.

Sampling error is only one type of error to which surveys are subject. Results may also be affected by factors such as question wording, question order, and survey timing. We present results for five geographic regions, accounting for approximately 90 percent of the state population.

Residents of other geographic areas are included in the results reported for all adults, registered voters, and likely voters, but sample sizes for these less-populous areas are not large enough to report separately.

A footnote in Microsoft's submission opens in new tab to the UK's Competition and Markets Authority CMA has let slip the reason behind Call of Duty's absence from the Xbox Game Pass library: Sony and Activision Blizzard have a deal that restricts the games' presence on the service. The footnote appears in a section detailing the potential benefits to consumers from Microsoft's point of view of the Activision Blizzard catalogue coming to Game Pass.

What existing contractual obligations are those? Why, ones like the "agreement between Activision Blizzard and Sony," that places "restrictions on the ability of Activision Blizzard to place COD titles on Game Pass for a number of years". It was apparently these kinds of agreements that Xbox's Phil Spencer had in mind opens in new tab when he spoke to Sony bosses in January and confirmed Microsoft's "intent to honor all existing agreements upon acquisition of Activision Blizzard". Unfortunately, the footnote ends there, so there's not much in the way of detail about what these restrictions are or how long they'd remain in effect in a potential post-acquisition world.

Given COD's continued non-appearance on Game Pass, you've got to imagine the restrictions are fairly significant if they're not an outright block on COD coming to the service.

Either way, the simple fact that Microsoft is apparently willing to maintain any restrictions on its own ability to put first-party games on Game Pass is rather remarkable, given that making Game Pass more appealing is one of the reasons for its acquisition spree.

The irony of Sony making deals like this one while fretting about COD's future on PlayStation probably isn't lost on Microsoft's lawyers, which is no doubt part of why they brought it up to the CMA.

While it's absolutely reasonable to worry about a world in which more and more properties are concentrated in the hands of singular, giant megacorps, it does look a bit odd if you're complaining about losing access to games while stopping them from joining competing services. We'll find out if the CMA agrees when it completes its in-depth, "Phase 2" investigation opens in new tab into the Activision Blizzard acquisition, which is some way off yet.

For now, we'll have to content ourselves with poring over these kinds of corporate submissions for more interesting tidbits like this one. So far, we've already learned that Microsoft privately has a gloomy forecast for the future of cloud gaming opens in new tab , and that the company thinks Sony shouldn't worry so much since, hey, future COD games might be as underwhelming as Vanguard opens in new tab.

Who knows what we'll learn next? Sign up to get the best content of the week, and great gaming deals, as picked by the editors. One of Josh's first memories is of playing Quake 2 on the family computer when he was much too young to be doing that, and he's been irreparably game-brained ever since.

His writing has been featured in Vice, Fanbyte, and the Financial Times. He'll play pretty much anything, and has written far too much on everything from visual novels to Assassin's Creed. His most profound loves are for CRPGs, immersive sims, and any game whose ambition outstrips its budget. He thinks you're all far too mean about Deus Ex: Invisible War.

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